Device and method for detecting wind noise

ABSTRACT

A device for detecting the presence of wind noise in an array of microphones including two or more separate sound inlet openings, and a sound to electrical converting element or microphone in relation to each sound inlet opening. The microphones each generate time dependent signals which are fed to a signal processing device that provides one or more output signals. The signal processing device has means for generating a time dependent cross correlation function between a first and a second microphone signal, and means for generating a signal corresponding to a time dependent auto correlation function of either the first or the second of the microphone signals. Further, the signal processing device has means for comparing the values of the auto correlation function and the cross correlation function and the means for comparing are arranged to detect the condition that the auto correlation function is substantially higher than the cross correlation function, whereby the condition is indicative of the presence of wind noise.

AREA OF THE INVENTION

The invention relates to a device for detecting the presence of windnoise in an array of microphones. The device comprises two or moreseparate sound inlet openings, and a sound to electrical convertingelement or microphone in relation to each sound inlet opening, where themicrophones generates each their time dependant signals and where saidsignals are fed to a signal processing device which provides one or moreoutput signals. The output signals can be used in various audio systems,such as hearing aids, headsets, telephones or wireless microphones.

The invention also relates to a method of detecting wind noise in asystem having more than one microphone.

BACKGROUND OF THE INVENTION

In audio systems comprising directional microphones or microphonearrays, it has been a problem that wind noise is generated even at verylow wind-speeds. It has been attempted to solve the problem by placing awindscreen in front of the microphone sound inlet opening, but thisinevitably results in reduced overall performance of the microphone.This is known in hearing aid with directional microphones or with two ormore microphones and DSP systems for generating an output withdirectionality.

In: “Digital Signal Processing, Principles, Algorithms, andApplications” by John G. Proakis et al. it is explained how wind noiseis a seismic signal (a signal created by nature) in the range from 100to 1000 Hz. In micro-phone systems with two or more sound inlet portsthe wind noise is generated by local air turbulence around the inletopenings, and therefor the sound signals received at the microphoneswill be uncorrelated. It is an object of the invention to use the natureof the wind noise signal to detect the presence of wind noise inmicrophone systems with two or more sound inlet openings and two or moreindependent electrical outputs.

The invention comprises a device of the above kind, where the signalprocessing device has means for generating a first time dependantcorrelation signal composed of cross correlation function values betweena first and a second microphone signal and, means for generating asecond time dependant correlation signal composed of auto correlationfunction values of either the first or the second of the microphonesignals and where the signal processing device has means for comparingthe values of the first and the second correlation signal and that themeans for comparing are arranged to detect the condition that the secondcorrelation signal value is higher than the first correlation signalvalue, whereby said condition is indicative of the presence of windnoise.

This device is very simple to implement either as a digital or as ananalog device. In digital devices the processing powers needed tocalculate the cross correlation function values is limited, and thecomparing means are also standard devices in both digital and analogdevices. When wind noise is detected, this information can be used in anumber of different ways. A wind noise-damping filter can be switchedon, or instead of array processing, the signal from a singleomnidirectional microphone may be used in the signal processing deviceto generate the output.

In a further aspects, the invention comprises a device for detecting thepresence of wind noise in an array of microphones comprising two or moreseparate sound inlet openings, and a sound to electrical convertingelement or microphone in relation to each sound inlet opening, where themicrophones generate time-dependent signals and where said signals arefed to a single processing device which provides one ore more outputsignals, the signal process device including means for combining themicrophone signals in order to form a single directional signal andwhere further the signal processing device has means for forming a firsttime dependent correlation signal composed of auto correlation functionvalues from one of the microphone signals before said signal is combinedwith the other signals and further has means for forming a second timedependent correlation signal composed of auto correlation functionvalues of the single directional signal and where the signal processingdevice has means for comparing the value of the first correlation signaland the second correlation signal and that the means for comparing arearranged to detect the condition that the second correlation signalvalue is higher than the first correlation signal value, whereby saidcondition is indicative of the presence of wind noise.

In this aspect of the invention the nature of the directional algorithmis used, and only the auto correlation values of the input signals tothe directional algorithm and the auto correlation values of the outputdirectional signal is generated, and on the basis of their mutual sizeit is determined whether wind noise is present or not.

In an embodiment of the invention the device comprises a low pass filterbetween the microphones and the means for generating the correlationsignals. As wind noise typically is in the frequency range of 100 Hz to1000 Hz the signal used in the detecting device does not need to haveany high frequency components. Further the limitation to frequenciesbelow 1000 Hz allows the process to be run down sampled (in digitalsystems), and this saves processing powers and energy.

It is preferred that each of the correlation functions are generatedcontinually using only single signal values at a given point in time. Inthe digital case, this means that squaring each of the sample valuesgenerates the short term autocorrelation in lag zero values, andmultiplying single point signal values from the respective microphonesgenerates the short term cross correlation value in lag zero. These arevery simple signal processing schemes in the digital domain, but alsofor analog signal processing, similarly simple processing can do this.The formulas for an autocorrelation and a cross correlation arer _(xx)(l)=<x(n)x(n−l)>(Autocorrelation)r _(xy)(l)=<x(n)y(n−l)>(Crosscorrelation)

In the described embodiment of the invention l is set to 0, so thecorrelation value is in both cases generated by one simplemultiplication. If n=0 is the present sample, a segment will be chosenform n=−k to n=k for a practical calculation of the correlation. In thepresent embodiment k is set to 0, and the correlation degenerates to asimple multiplication.

In a preferred embodiment a mean value generator is provided for each ofthe correlation functions. In digital processing this could be done by asimple IIR filter having the following form:H(z)=1/(1−a ₁ *z ⁻¹)

The value a₁ determines the weight of the previous samples with respectto the present sample, and thus determines the dynamic behavior of thesystem. A suitable value for a₁ is 0.9999 at 16 kHz sampling frequency.Many other IIR or FIR filters may produce values, which gives a goodrepresentation of the mean value at a given time. Also for analoginstruments such a mean value generator is simple to implement, eg. asan integrator with loss.

According to the a further embodiment of the invention the means forcomparing the auto correlation function in lag zero with the mean valueof the cross correlation function in lag zero are arranged to determinethat wind noise is present whenever the estimated value of the autocorrelation function is more than 1.5, preferably more than 2.0 timesbigger than the estimated value of the cross correlation function.Thereby it is ensured, that whenever the wind noise becomes so loud,that it is perceived as annoying the signal processor gets a messagefrom the comparing means, and appropriate measures can be taken tolessen the effect of the wind noise.

According to yet another embodiment, the means for comparing aredesigned to only become active whenever a given level of signal energyin the microphone channels is detected. This is important, because insome systems the noise generated by the microphones themselves isconsiderable, and as this noise is also un-correlated, it may triggerthe wind detection mechanism, even if there is no air circulation at allaround the sound inlet openings.

In a further aspect the invention concerns a method for detecting thepresence of wind noise in a system comprising two or more microphoneelements having each their sound inlet openings, where first correlationsignal is generated composed of the cross correlation values between afirst and a second microphone signal and where further a secondcorrelation signal is generated composed of auto correlation functionvalues of either the first or the second of said microphone signals, andwhere the values of the first correlation signal and the secondcorrelation signal are compared, and that a wind noise indicator isactivated whenever the value of the second correlation signal is higherthan the value of the first correlation signal.

In a further aspect of the method according to the invention the systemcomprises two or more microphone elements having each their sound inletopenings. Here the microphone signals are combined in order to generatea single directional signal. A first correlation signal is generatedfrom composed of auto correlation function values of one of saidmicrophone signals and also a second correlation signal is generatedcomposed of auto correlation function values of the directional signal,and the value of the first correlation signal is compared to the valueof the second correlation signal, and a wind noise indicator isactivated whenever the value of the second correlation signal is higherthan the value of the first correlation signal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a basic model of the noise in a two-microphone system,

FIG. 2 is a diagram showing the signal processing elements of the windnoise detecting system according to the invention.

FIG. 3 is a diagram showing the signal processing elements in thedirectional system with two microphones in a directional algorithm forcombining the signals from the two microphones.

DESCRIPTION OF A PREFERRED EMBODIMENT

FIG. 1 shows a system with two microphones and an external sound sources(t). The time delay from the sound source s(t) to the two microphonesis t1 and t2 respectively. The distance between the two microphones isd. The wind noise in each microphone is represented as a noise source e₁and e₂ respectively. These two noise sources are un-correlated, whichmeans that cross correlation between e₁ and e₂ is approximately zero.The output from the microphones will be:x(t)=s(t−t1)+e ₁(t)y(t)=s(t−t2)+e ₂(t)

The autocorrelation at lag zero of x (r_(XX)) and the maximum of theautocorrelation of y (r_(YY)) consist of the energy of: the externalsound source s(t), the wind noise and the internal noise of themicrophone. The contribution of the internal noise is considerednegligible at this point and is dealt with later. The wind noise is anexternal noise source, but due to its un-correlated nature it can bemodeled as an internally generated signal. Mathematically theautocorrelations of the signals x and y can be expressed like this:r _(XX)(l)=r _(SS)(l)+r _(e1,e1)(l)r _(YY)(l)=r _(SS)(l)+r _(e2,e2)(l)

Mathematically the cross correlation between x(t) and y(t) can bewritten as:r _(XY)(l)=r _(SS)(l+t1−t2)+r _(e1,e2)(l)

As the correlation between e1 and e2 is approximately zero we are ableto approximate with:r _(XY)(l)=r _(SS)(l+t1−t2)

In a wind noise situation the value of the cross correlation is smallerthan the value of the auto correlation, which is what this wind noisedetector uses, i.e. r_(XY)<r_(XX) or r_(XY)<r_(YY) since r_(e1,e2)remains approximately zero and r_(e1,e1)(l) or r_(e2,e2)(l) grows withgrowing wind noise. Because the wavelength of the highest frequency ismuch longer than the distance between the microphones, t1-t2 isapproximately zero.

In FIG. 2 the system from FIG. 1 is shown with the signal processing. Inthis system a correlation length of 1 sample is used. The systemrequires that the microphones distance d is much smaller than thewavelength of the highest frequency. If the distance d is large so thatt1-t2 is not approximately zero, longer lag times in the correlationcalculation is needed, and the maximum value of the correlation ispassed on as the result.

The described system will work with a sampling frequency of 16 kHz. Inthe system an analog to digital converter (not shown) is placed beforethe low pass filter LP in FIG. 2. The system is equipped with 2 hearingaid microphones EM-type from Knowles with a preamplifier.

The low pass filter is a second order Butterworth filter (biquad), whichhas a cut off frequency of 1000 Hz. This low pass filter makes sure thatonly those frequencies, which are of interest, are fed to the wind noisedetection system. Further, it gives the opportunity to run theprocessing down sampled. A FIR filter would produce the same result. Thewavelength of a 1000 Hz tone is approximately 32 cm., which is muchlonger than the distance between the two microphones. As earlierdescribed; if the distance between the microphones means that thedifference between the t1 and t2 is not approximately zero. In thatcase, one needs to calculate a longer lag space and find the maximum,i.e., where the difference between t1 and t2 is approximately zero.

In the box X*X in FIG. 2 the value of the auto correlation in samplezero (r_(x,x)(0)) of the low pass filtered signal from the microphone iscalculated. In this system we square the signal, which is the same as ashort term energy measure. In the box X*Y in FIG. 2 we calculate thevalue of the cross correlation in sample zero (r_(x,y)(0)). If thedistance between the two microphones is large, the value t1-t2 is notapproximately zero and one need to calculate a larger correlation lengthand find the maximum.

A way to calculate the true mean value is to summarize all data anddivide with the number of data. This version is for obvious reasons notpossible to implement. A method that that can be implemented is tocalculate in segments. Another way is to feed the sample through asuitable IIR filter, preferably a first order IIR filter. The chosenfilter can be described in the following way:H(z)=1/(1−a ₁ *z−1)=1/(1−0.9999*z−1)

The filter is not very critical and can be implemented in various otherways (IIR or FIR). The value of a₁ is comparable to a time constant, anddetermines the reaction time to shifts in wind noise level.

In the decision box it is determined whether there is wind noise or not.In the present example of the invention the decision limit is set suchthat wind noise is detected when the auto correlation mean value is morethan twice as large as the calculated cross correlation mean value.Because of the relative large amount of uncorrelated microphone noise,decision limit as a function of the energy is also incorporated into thedecision algorithm. In this way there has to be some acoustical input tothe microphones before the above limit will be calculated. This problemis mostly related to hearing aid microphones or other nosy microphones.

It is also possible to implement the system in an entirely analogversion. The system has the same dataflow as shown in FIG. 2, but someof the boxes can be implemented in another way. First, the mean valuecalculator can be implemented as an integrator with loss. Mathematicallythis is nearly the same as the digital version described above. Thedecision box then relates to a comparator in the analog domain. Thefilters and multiplicators are normal analog building blokes.

Another way of designing a wind noise detector is to make use of thedirectionality (fx. Gary Elko, U.S. Pat. No. 5,473,701). In thisalgorithm we use the difference between the autocorrelation of one ofthe inputs and the autocorrelation of the outputs. The system is shownin FIG. 3. The wind noise measure system uses that the wind noise in theinput channel X or Y consist of target signal s(t) and the signal fromthe wind noise e₁ or e₂ respectively. Then the auto correlation functionis:r _(xx)(l)=r_(ss)(l)+r _(e1,e1)(l) or r _(yy)(l)=r_(ss)(l)+r_(e2,e2) (l)

The auto correlation from the output signal of the directional algorithmconsists of the correlation of the autocorrelation of the target signaland the auto correlation of both the wind noise sources. i.e.r _(DIR,DIR)(l)=a*r _(ss)(l)+r _(e1,e1)(l)+r _(e2,e2)(l) (a is a scalingfactor from the directional algorithm)

The system works then by detecting the difference between the inputautocorrelation r_(XX) or r_(YY) and the output autocorrelation of thedirectional system r_(DIR,DIR). For large values of wind noise we haver_(DIR,DIR)>r_(XX) or r_(DIR,DIR)>r_(YY).

The calculation of the correlation values can be made as earlierdescribed. The decision should also be the same but the input signals tothe decision box are measured in different places. The boundary betweendetection wind noise and not are the same as in the first describedsystem because of the missing noise signal (the signal, which thedirectionality algorithm removes). In the real world the signal wouldonly be damped because of reverberations and therefore the decisionboundary should be a variable of how efficient the directional algorithmis.

1. Device for detecting the presence of wind noise in an array ofmicrophones comprising two or more separate sound inlet openings, and asound to electrical converting element or microphone in relation to eachsound inlet opening, where the microphones generates each their timedependant signals and where said signals are fed to a signal processingdevice which provides one or more output signals where the signalprocessing device has means for generating a first time dependantcorrelation signal composed of cross correlation function values betweena first and a second microphone signal and, means for generating asecond time dependant correlation signal composed of auto correlationfunction values of either the first or the second of the microphonesignals, wherein the signal processing device has means for comparingthe values of the first and the second correlation signal and the meansfor comparing are arranged to detect a condition when the secondcorrelation signal value is higher than the first correlation signalvalue, whereby said condition is indicative of the presence of windnoise, and wherein the means for comparing the first correlationfunction with the second correlation function are arranged to determinethat wind noise is present whenever the mean value of the secondcorrelation function is more than 1.5 times larger than the mean valueof the first correlation function.
 2. Device as claimed in claim 1,including a low pass filter between the microphones and the means forgenerating the correlation signals.
 3. Device as claimed in claim 1,including a mean value generator for each of the correlation signals. 4.Device as claimed in claim 1, where the means for comparing are designedto only become active whenever a given level of signal energy in themicrophone channels is detected.
 5. Device for detecting the presence ofwind noise in an array of microphones comprising two or more separatesound inlet openings, and a sound to electrical converting element ormicrophone in relation to each sound inlet opening, each microphonegenerating time-dependant signals, and a signal processing device towhich the time-dependent signals are fed, said signal processing deviceincluding means for combining the microphone signals in order to form asingle directional signal, means for forming a first time dependantcorrelation function composed of auto correlation function values fromone of the microphone signals before said signal is combined with theother signals, means for forming a second time-dependant correlationsignal composed of auto correlation function values of the singledirectional signal, and comparing means for comparing the values of thefirst correlation signal and the second correlation signal, saidcomparing means detecting a condition when the second correlation signalvalue is higher than the first correlation signal value, indicating thepresence of wind noise.
 6. Device as claimed in claim 5, including a lowpass filter between the microphones and the means for generating thecorrelation signals.
 7. Device as claimed in claim 5, including a meanvalue generator for each of the correlation signals.
 8. Device asclaimed in claim 5, wherein the comparing means for comparing the firstcorrelation function with the second correlation function are arrangedto determine that wind noise is present whenever the mean value of thesecond correlation function is more than 1.5 times larger than the meanvalue of the first correlation function.
 9. Device as claimed in claim5, where the means for comparing are designed to only become activewhenever a given level of signal energy in the microphone channels isdetected.
 10. Method for detecting the presence of wind noise in asystem comprising two or more microphone elements having each theirsound inlet openings, where a first correlation signal is generatedcomposed of the cross correlation values of a first and a secondmicrophone signal, and where further a second correlation signal isgenerated composed of auto correlation function values of either thefirst or the second of said microphone signals, and where the firstcorrelation signal and the second correlation signal are compared, andthat a wind noise indicator is activated whenever the value of thesecond correlation signal is higher than the value of the firstcorrelation signal, where the condition for wind noise detection is metwhenever the mean value of the second correlation function is more than1.5 times larger than the mean value of the first correlation function.11. Method as claimed in claim 10, where the signals from themicrophones are passed through a low pass filter before the correlationsignals are generated.
 12. Method as claimed in claim 1, where each ofthe correlation functions are generated continually using only a singlevalue at a given point in time.
 13. Method as claimed in claim 10, wherea mean value is generated for each of the correlation function signals.14. Method as claimed in claim 10, where the means for comparing onlybecome active whenever a given level of signal energy in the microphonechannels is detected.
 15. Method for detecting the presence of windnoise in a system comprising two or more microphone elements having eachtheir sound inlet openings, and where the microphone signals arecombined in order to generate a single directional signal, and where afirst correlation signal is generated composed of the auto correlationfunction values from one of said microphone signals and where a secondcorrelation signal is generated composed of auto correlation functionvalues from the directional signal, and where the value of the firstcorrelation signal is compared to the value of the second correlationsignal, and a wind noise indicator is activated whenever the value ofthe second correlation signal is higher than the value of the firstcorrelation signal.
 16. Method as claimed in claim 15, where the signalsfrom the microphones are passed through a low pass filter before thecorrelation signals are generated.
 17. Method as claimed in claim 15,where each of the correlation functions are generated continually usingonly a single signal value at a given point in time.
 18. Method asclaimed in claim 15, where a mean value is generated for each of thecorrelation function signals.
 19. Method as claimed in claim 15, wherethe condition for wind noise detection is met whenever the mean value ofthe second correlation function is more than 1.5 times larger than themean value of the first correlation function.
 20. Method as claimed inclaim 15, where the means for comparing only become active whenever agiven level of signal energy in the microphone channels is detected.